Performance metrics

ABSTRACT

A method and system relates to locating or otherwise generating positional information for an object, such as but not limited generating positional coordinates for an object attached to an athlete engaging in an athletic event. The positional coordinates may be processed with other telemetry and biometrical information to provide real-time performance metrics while the athlete engages in the athletic event.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. application Ser. No.12/657,217 filed Jan. 15, 2010 now U.S. Pat. No. 8,330,611, entitled“Positional Locating System and Method”, which, in turn, claims thebenefit of U.S. provisional application Ser. No. 61/205,146 filed Jan.15, 2009, entitled “Method and System of Object Tracking and Telemetry”,and U.S. provisional application Ser. No. 61/287,361 filed Dec. 17,2009, entitled “Method and System of Object Tracking and Telemetry”.This application further claims the benefit of U.S. ProvisionalApplication 61/424,439 filed Dec. 17, 2010, entitled “PerformanceMetrics”. The disclosures of which are incorporated in their entirety byreference herein.

BACKGROUND OF THE INVENTION

1. Technical Field

The present invention relates to systems and methods of positionallylocating moving objects and real-time generation of telemetry andperformance metrics for the moving objects.

2. Background Art

Global positioning system (GPS) can be used to positionally locate amoving object. GPS relies on a GPS enabled device attached to the objectto calculate positional coordinates based on information transmittedfrom orbiting satellites. The reliance on orbiting satellites can beproblematic while the moving object is within a covered area since theGPS enabled device may not receive the signals necessary to perform itscoordinate calculations.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is pointed out with particularity in the appendedclaims. However, other features of the present invention will becomemore apparent and the present invention will be best understood byreferring to the following detailed description in conjunction with theaccompany drawings in which:

FIG. 1 illustrates a locating system in accordance with one non-limitingaspect of the present invention;

FIG. 2 illustrates spatial reduction in accordance with one non-limitingaspect of the present invention;

FIG. 3 illustrates performance telemetry in accordance with onenon-limiting aspect of the present invention;

FIG. 4 illustrates a graphical user interface in accordance with onenon-limiting aspect of the present invention;

FIGS. 5-6 illustrate head and limb devices in accordance with onenon-limiting aspect of the present invention;

FIG. 7 illustrates an exemplary set of accelerometer data in accordancewith one non-limiting aspect of the present invention;

FIG. 8 illustrates a coordinate system used in accordance with onenon-limiting aspect of the present invention;

FIG. 9 illustrates a flowchart of a method for generating performancemetrics in accordance with one non-limiting aspect of the presentinvention;

FIG. 10 illustrates a flowchart for stroke identification in accordancewith one non-limiting aspect of the present invention;

FIGS. 11 a-11 b illustrate one such comparison process as contemplatedby one non-limiting aspect of the present invention;

FIG. 12 illustrates a flowchart for stroke identification in accordancewith one non-limiting aspect of the present invention;

FIG. 13 illustrates a graph of accelerometer data in accordance with onenon-limiting aspect of the present invention;

FIG. 14 illustrates a histogram plot in accordance with one non-limitingaspect of the present invention;

FIG. 15 illustrates a matching plot in accordance with one non-limitingaspect of the present invention;

FIG. 16 illustrates a graph of leg accelerometer data filtered accordingto a kick counting filtering process contemplated by the presentinvention;

FIG. 17 illustrates a flowchart of a kick counting process in accordancewith one non-limiting aspect of the present invention;

FIG. 18 illustrates a sinusoidal kicking pattern in accordance with onenon-limiting aspect of the present invention;

FIG. 19 illustrates an impulse kicking pattern in accordance with onenon-limiting aspect of the present invention;

FIG. 20 illustrates a graph of peak identification in accordance withone non-limiting aspect of the present invention; and

FIG. 21 schematically illustrates determination of lap events inaccordance with one non-limiting aspect of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT(S)

FIG. 1 illustrates a locating system 10 in accordance with onenon-limiting aspect of the present invention. The system 10 may includeon one or more cameras 12 to facilitate locating one or more devices 14as the devices 14 travel within an area covered by the cameras 12. Thedescription set forth below is predominately described with respect tothe cameras 12 being positioned around a swimming pool in order tofacilitate locating the devices 14 while the devices 14 are being wornby swimmers swimming within the swimming pool. In this example, thedevices 14 are referred to as head devices 14 and a master station 16may be configured to facilitate control of the head devices while thecameras 12 capture related images in order to determine a position ofthe swimmers with the swimming pool. The position of each swimmer can becalculated from a spatial reduction of the signals captured within theimages to particular positions within the pool.

FIG. 2 schematically illustrates one example of the spatial reductiontechnique contemplated by the present invention where one of the cameras12 may be positioned relative to a swimming pool 18 to capture imageframes 20, 22 of events taking place within its viewing angle 24. Whilethe present invention fully contemplates the image frames 20, 22including visual and/or non-visual representations of any capturedsignals, the images frames shown in FIG. 2 represent images frames 20,22 captured with an infrared (IR) camera 12 configured to capture IR orother non-visible representations of signals emitted from one or morebeacons 28, 30 included on head devices 14 of on a first and secondswimmer (1, 2) that happen to be swimming in the area covered by thecamera's viewing angle 24 at a first time (T1) and second time (T2). Thecamera 12 may include a filter or other configuration particularlysuited to capturing the signals emitted from the beacons 28, 30.

The first image frame 20 captured at time T1 and the second image frame22 captured are time T2 are shown to be of the type where the camera 12was configured to filter signals other than those that correspond withthe beacons 20, 22. The captured signals are shown with circularrepresentations for exemplary purposes only. Depending on the resolutionof the camera 12, the wavelength and strength of the signal may exhibitsome other pattern or representation within one or more of pixels (notshown) of the image frames 20, 22. A pixel-by-pixel analysis of theimage frames 20, 22 may be performed with an image processing element 32(see FIG. 1) of the master station 16 to identify the pixels thatrepresents receipt of the strongest signal. Depending on the positioningof the camera 12 at the time the image frames 20, 22 were captured, eachof the pixels can be associated with a particular area within theviewing angle 24, i.e., to a particular location/position within thepool 18. The pixel having the strongest signal can act as a center forcoordinate generation purposes.

The arrangement shown in FIG. 2 includes the camera 12 being set a fixedposition above the swimming pool 18 in order to simplify the spatialreduction of the images captured within the image frames 20, 22 toparticular areas in the pool 18. Since the position of the camera 12 isknown and fixed, a limited number of calculations are need to associateeach pixel of the image frames 20, 22 with a known portion of theswimming pool 18. This example, however, is not intended to limit thescope and contemplation of the present invention. The present inventionfully contemplates the camera 12 moving such that additional processingmay be required to fix the position of the camera 12 at the time ofimage capture before the areas associated with each image pixel could beidentified with a particular area of the swimming pool 18.

Each of the pixels chosen to be representing the center of the emittedsignals may be associated with an X-axis and Y-axis coordinate valueaccording to an identity of the swimmer (shown as swimmer #1 and swimmer#2) and a timestamp representative of a time at which each image framewas taken (shown as timestamp #1 and timestamp #2). Using thismethodology, a coordinate value (XST, YST) can be assigned to a locationof each swimmer within the captured image frames 20, 22 where Xidentifies a position along the X-axis, Y identifies a position alongthe Y-axis, S identifies the swimmer, and T identifies the timestamp.This image-based coordinate may be defined relative a positioning of thecamera 12 capturing the associated image and may be sufficient todetermine a location from a single image, as opposed to having to takemultiple pictures before fixing the location. In some cases, multiplecameras 12 may be used to capture images of different and/or overlappingportions of the pool. The resulting image-based coordinates may bedefined locally to that associated camera 12 and thereafter the spatialrelationship of one or more the cameras 12 may be used reduce theimage-based coordinates into some other coordinated system and/or tocheck the relative accuracy/precision of the other coordinates.

A location computer 34 may be configured to host the image process unit32 and to output the image-based coordinate values as rawrepresentations of the swimmer's location within the images 20, 22and/or the image processing unit 32 may be configured to convert orotherwise process the image coordinates into world coordinates, globalposition system (GPS) coordinates, and/or some other coordinateorientation that has an absolute coordinate other than the camera orpool 18. The coordinate information may then be communicated to a hostcomputer 36 for further processing. The host computer 36 may include aserver 38 to host a planning application 40 and a training application42 along with middleware 44 and temporary data storage 46 to facilitateinteraction with other elements of the system 10 and to otherwisesupporting data and processing demands necessary to supporting theoperations contemplated herein.

The train application 42 may be a tool that tracks and otherwiseprocesses telemetry information for the swimmers based at least in parton the coordinate information output from the location computer 34. FIG.2 illustrates a performance telemetry image frame 48 to illustrate howsome of the metrics contemplated by the present invention may becalculated based on the coordinate values, such at but not limited todistance traveled and speed. The performance telemetry image frame 48can be used as a superimposition of two or more of the image frames 20,22 where movement of the swimmers between images frames can begraphically illustrated with a first reference vector 50 and a secondreference vector 52. The length of the vectors 50, 52 can be used torepresent distance traveled which can then be used to calculate speedaccording to a time elapsed between the first and second timestamps.

The exemplary illustration of distance and speed is not intended tolimit the scope and contemplation of the present invention as thepresent invention fully contemplates including the coordinatedrepresentations in any type of a calculation, particularly calculationsthat are specified to the actions and movements of element being trackedwith the device 14. FIG. 3 illustrates a display 60 of exemplaryperformance metrics that may be generated based at least in part on thecoordinate values. In some cases, the performance telemetry may be basedin part on additional information collected from the swimmers, such asbased on information collected from limb devices 62 attached to arms andlegs of the swimmers (see FIG. 1). The limb devices 62 may be includeaccelerometers or other devices configured to monitor and reportmovements of the swimmer to the master station 16, such as throughwireless communications. The data transmitted from the limb devices 62may be sent in real-time and coordinated with the real-time calculationof the coordinate values to provide a real-time analysis of the swimmersactions, i.e., stroke rate, stroke style, speed, splits, etc.

The real-time telemetry may be output on a user interface of a hand-helduser device 66 (see FIG. 1) connected to the master station 16 by way ofa wireless node 68, such as but not limited to a tablet computer used bya coach to monitor swimmer performance. FIG. 4 illustrates one exemplaryconfiguration of a graphical user interface 70 where the telemetryinformation of a particular swimmer is display with numerical values ofcurrent performance, and optionally, with color coded references towhether the swimmer is performing at, below, or above pastaverages/metrics. The performance telemetry may be displayed relative toa video window 72 showing a real-time video of the selected swimmermoving through the swimming pool. The video may be captured with one ormore video cameras 74 (see FIG. 1) positioned around the swimming pool.The feeds from the video cameras 74 may be spliced together to provide acontinuous video as the feed switches with movement of the swimmerbetween the cameras 74 placed at different locations along the pooland/or one or more of the cameras 74 may be configured to automaticallymove with the swimmer according to positional information determinedfrom the coordinate values.

A central server 78 and one or more personal computers 80 may beincluded in the system 10 (see FIG. 1) to facilitate storing theperformance telemetry for any number of swimmers and to allow theswimmers to retrieve the telemetry data and recorded video at later timefor review. The central server 78 may include a billing application 82and registration application 84 to facilitate managing online contentand subscription based services to the performance telemetry. Onlinesocial networks may be established to facilitate remote training andcompetitions between swimmers swimming at different swimming poolsacross the country. One of the advantageous contemplated by the presentinvention relates to its ability to provide real-time feedback duringpractice and retrieval feedback at a later time, such as to support filmstudy and review by the swimmer themselves. The system 10 supports afull spectrum of performance telemetry and is particularly suited toperformance telemetry of the type that is based at least in part onobtaining a positional location of the tracked entity.

The positional tracking described above relies on a camera 12 or othersensor to positionally orientate a signal from one or more beacons 28,30 included on a swimmer. This methodology is believed to be particularbeneficial to supporting the real-time performance telemetrycontemplated by the present invention since it allows the master station16 or some other entity other than the head device 14 to perform thepositional calculations. This can be helpful in reducing the size,complexity, and cost of the head device 14, although the presentinvention fully contemplates configuring the head device 14 to calculatetheir own positional coordinates through wireless communication andprocessing of the image frames 20, 22. The head device contemplated byone non-limiting aspect of the present invention, however, may includewireless communication capabilities and other capabilities necessary toimplementing the objectives of the present invention, such as but notlimited to having an transceiver (not shown) to support one-way ortwo-way information exchange and processing over a wireless systemestablished with the master station.

The wireless system may be used by both of the limb and head devices 14,62 to facilitate wireless communications with the master station 16. Thelimb device communications may be used to transmit accelerometer,biometric, and other types of data collected by the limb devices 62 tothe master station 16. The wireless communications may be facilitatedwith the use of MAC address or other unique addresses assigned to eachof the head and limb devices 14, 62 so that messages sent to andreceived from the head devices 14, 62 can be uniquely identified andcontrolled. The master station 16 may include a table or otherrepresentation to further correlated the MAC address with particularswimmers, such as by requiring each swimmer to log-in prior to practiceor to otherwise identify themselves with a particular head device. Themaster station 16 may further associate the head device with aparticular signature or other unique representation to be output fromthe associated beacon 28, 30 so that each beacon 28, 30 shown within theimage frames can be identified with a particular swimmer.

One non-limiting aspect of the present invention contemplates thebeacons 28, 30 emitting signals with a self-identifying pattern (such asbut not limited to emitting signals at particular sequence or flashrate) and/or at a predefined interval of time. The self-identifyingpattern may be a slightly more complex signature since it may requirethe master station 16 to assign and decipher unique patterns for anumber of head devices 14, which can be difficult should there be 50 or100 swimmers being tracked at the same time. The predefined intervalassignment process may be less complex since it may only require themaster station 16 to monitor an epoch assigned to each of the headdevices 14 for beacon transmissions. Each beacon 28, 30, optionally, maybe assigned a unique epoch where it is the only beacon transmitting asignal at a particular period of time. In this scenario, the timestampassociated with each image frame can be cross-referenced with the beacon28, 30 assigned to transmit during that period of time to identify theone or more beacons 28, 30 within the image frame 20, 22. In thismanner, the present invention is able to generate positional coordinatelocations for the swimmer without requiring the head device 14 tocalculate its own position.

The head and limb devices 14, 62 may be battery powered andwaterproof/resistance to support wireless operation within the swimmingpool and other environments. A docketing station 90 having ports foreach set of the limb and head devices 14, 62 may be included in thesystem to facilitate battery charging, and optionally, software loadingand updating. FIG. 5 illustrates the head and limb devices 14, 62 beingparticularly configured to support operation with a swimmer inaccordance with on non-limiting aspect of the present invention. Thelimb device 62 may be configured for receipt within a wrist wrap 92 andan ankle wrap 94. The head device 14 may be configured with the firstbeacon 28 and the second beacon 30 that protrude through correspondingopenings within a swim cap 96.

The head device 14 is further shown to include an ear bud 100. The earbud 100 may be used to facilitate audio communications with the simmer.One form of audio communication may include the master station 16 orother device wireless transmitting verbal instructions from the coach tothe swimmer, such as through a microphone (not shown) included on thetablet device. Another form of audio communication may include the headdevice 14 being programmed, either wireless while the swimmer isswimming or before, to issue chimes and other audio references toswimmer to indicate distance traveled, stroke rhythm, etc. and/or tosimply play music. As shown in FIG. 6, the head device 14 may include adetachable pod 102 that can be connected through a USB port to thecharging station 90. The swim cap 96 may include an envelope 98 or otherstructure to position the first and second beacons 28, 30 relative tothe openings.

While the use of both of the first and second beacons 28, 30 is notrequired, it may be helpful to have one or more of the beacons 28, 30emitting the signal. Optionally, the master station 16 may assign eachof the first and second beacon 28, 30 their own transmission interval sothat only one beacon is transmitting within each image frame 20, 22. Thefirst beacon 28, for example, can be used when the swimmer is performinga breaststroke or other action where the rearward portion of the headmay be pointing away from the camera, such as towards a bottom of thepool or other direction where refraction/reflection may prevent a properreading of the signal from the rearward beacon 30. Similarly, thesecond, rearward beacon 30 may be used when the swimmer is performing afreestyle stroke or other action where the forward portion of the headmay be pointing away from the camera, such as towards a bottom of thepool or other direction where refraction/reflection may prevent a properreading of the signal from the forward beacon 28. The emitting beacon28, 30 may be selected based on accelerometer data collected from thelimb device 62, such as through direct wireless communications betweenthe limb and head devices 62, 14 or by way of master station 16 wirelesscommunications, and/or the head device 14 may include an accelerometerto make its own determination.

FIG. 7 illustrates an exemplary set of accelerometer data 200 collectedby an accelerometer included within the head device 14 while a swimmerperforms each of a butterfly, back, breast and crawl stroke. Similaraccelerometer data may be captured by the limb devices 62 (referredinterchangeably as a leg sensor 94 when attached to a leg and armsensors 92 when attached to an arm) to reflect movement of the relatedbody part while swimming and/or while performing other types ofmovements. The present invention is particularly described with respectto generating performance metrics while swimming but the presentinvention fully contemplates its application in generating performancemetric for other types of movements and athletic events/competitions.FIG. 8 illustrates a coordinate system 202 used to relate the x, y, andz values to movements within to x, y, and z directions detected with anaccelerometer.

One type of accelerometer contemplated by the present invention togenerate the illustrated data may be configured to sense a rate ofchange in velocity of all three axes/directions (i.e., x, y, and z).Other types of device capable of generating similar data may include agyroscope (absolute positional generation defined as pitch, yaw, androll), a magnetometer (dead reckoning), compass, etc. The accelerometermay include a weight attached to a lever arm to record the x, y, and zmovement according to measureable responses of the lever arm tomovement. This type of accelerometer may be configured to generate thex, y, and z values while experiencing any type of non-constant movement.The accelerometers or other device used in place thereof may also beconfigured to generate similar values when experiencing constant motion,however, since the performance metrics generated according to theprocesses described below are predicated upon the accelerometer relyingon recording velocity change, the present invention is describedpredominately without regard to the use of a constant motion detectingaccelerometer.

The data shown in FIG. 7 and the performance metrics generated by thepresent invention are described with respect to generic x, y, and zvalues in that the corresponding values may vary depending on thecharacteristics of the particular accelerometer being used. The notedvalues are therefore not intended to limit the scope and contemplationof the present invention as the present invention fully contemplates thevalues, and in some cases thresholds, being varied according to thecharacteristics of the accelerometer, processing capabilities of theprocessor performing the calculations, and/or other designconsiderations.

FIG. 9 illustrates a flowchart 206 of a method for generatingperformance metrics in accordance with one non-limiting aspect of thepresent invention. Block 208 relates to identifying a swimming strokebeing performed by a swimmer, or other movement in the event the presentinvention is used to generate performance metrics for non-swimming basedmovement. FIG. 10 illustrates a flowchart 210 for stroke identificationwhere a method is described for determining whether a swimmer swimmingwithin a swimming pool is performing a crawl stroke, back stroke,butterfly stroke, or breast stroke based on the accelerometer datacaptured by the arm sensors 92 and head sensor 14. The flowchart isdescribed without analyzing data collected by the leg sensors 94 as partof the stroke identification process since one non-limiting aspect ofthe present invention contemplates identify the swim stroke withoutreliance of on leg sensor data. The present invention, however, fullycontemplates relying on leg sensor data to perform the notedidentification or to confirm or refine the data collected form the armand head sensors 92, 14.

The flowchart 210 begins in Block 214 with analyzing the x, y, and zvalues to assess whether the swimmer is performing a swimming strokehaving an alternating arm pattern whereby the swimmer's arms move out ofsequence to each other when propelling the swimmer through the swimmingpool (i.e., crawl stroke and back stroke). Block relates to identifyingthe alternating arm pattern based on comparing the accelerometer datafor the left arm with the same data collected from the right arm. FIGS.11 a-11 b illustrate one such comparison process as contemplated by onenon-limiting aspect of the present invention. This comparison process,and other calculations/processes noted herein, may be performed byprocessors included on the swimmer devices 14, 62 (leg, arm, and headdevices) and/or with the master station 16 operating in paralleltherewith or independently thereof.

The alternating type stroke may be identified as a function how closelyaligned the left arm accelerometer data matches with the right armaccelerometer data for a certain period of time. FIG. 11 a illustrates asquare wave 218 generated from a graph 220 of the accelerometer datafrom one of the arm sensors 92. The square wave 220 marks the rising andfalling edges of the accelerometer data where the data experiences azero axis crossing. Returning to FIG. 8, the swimmer's movement throughthe pool, as reflected by the acceleration changes, indicates arelatively cyclical process, tending towards a sine wave type appearanceand in strokes having repeated zero axis crossings. These zero axiscrossing may be used to create the illustrated square wave 218. Theamplitude of each square is shown to be normalized to 1 or −1 dependingon the corresponding signal from which it is derived. While the actualamplitude may be greater than 1 or −1, these values are selected tosimplify further processing.

The alternating stroke identification process may include:

1. Each arm device bandpass filtering each axis of the accelerometerdata to remove the DC offset and high frequency noise;

2. Generating a square wave for each axis;

3. Multiplying the corresponding square waves axes from each armtogether (FIG. 11 b exemplarily illustrates square waves 224, 226generated for one axis of each arm and a wave 228 resulting from themultiplication thereof);

4. Averaging the multiplied values from each axis over a lap length;

5. Squaring the averages from each axis to accentuate axes with morephase discrimination; and

6. Summing the results from each axis, keeping the sign (+ or −) foreach. The summed result may then be compared to zero to identify whetheran alternating stroke was performed, e.g., a value >=0 indicates thelimbs are not alternating and a value <0 indicates the limbs arealternating.

The A value used to identify an alternating stroke in the describedmanner may be generated from the following formula:

$A = {{§\left( {\sum\limits_{i = 1}^{N}{X_{Ri}X_{Li}}} \right)}^{2} + {§\left( {\sum\limits_{i = 1}^{N}{Y_{Ri}Y_{Li}}} \right)}^{2} + {§\left( {\sum\limits_{i = 1}^{N}{Z_{Ri}Z_{Li}}} \right)}^{2}}$

wherein x_(L), y_(L), and z_(L) are values used by the left arm sensorto quantify movement in the corresponding x, y, and z directions;wherein x_(R), y_(R), and z_(R) are values used by the right arm sensorto quantify movement in the corresponding x, y, and z directions; andwherein § is a positive value of 1 when the corresponding x, y, and zvalue represents movement the positive direction and a negative value of1 when the corresponding x, y, and z value represents movement in thenegative direction.

An analysis of the swimmer's head orientation may be made in Block 232in the event an alternating swimming stroke is determined. The headorientation may be considered to be head-up when the forehead/face ofthe swimmer is pointing generally towards the bottom of the pool orforwardly towards the end of the pool as it would be positioned whenexecuting a crawl, breast, or butterfly stroke. The head orientation maybe considered to be head-down when the forehead/face of the swimmer ispointing generally upwards out of the pool as it would be positionedwhen executing a back stroke. The head orientation may be measured bylow-pass filtering the sum of the X and Z axes, accelerometer data suchas over a sampled period of time (e.g., every two seconds). Theresulting signal may then be averaged to generate a value H forcomparison to a threshold. Based on empirical testing, a threshold ofsix, which is a relative thresholds tied to the particular datagenerated by the accelerometers that was determined to be a sufficientindicator of the head orientation, i.e., being orientated to thehead-down position if less than six and in the head-up position ifgreater than six.

The H value used to arbitrate between the head-up and head-downorientation may be determined according to the following formula:H=x _(H) +z _(H);

wherein x_(H) and z_(H) are values used by the head sensor to quantifymovement in the corresponding x and z directions. The y-axis is shown asnot being considered as part of the head orientation determinationbecause it tends not to experience as much movement as the x and z axes,making it potentially more difficult to filter, and because the x an zaxes were determined to be sufficient, allowing extra filtering of they-axis to be omitted.

A back stroke is determined in Block 234 in the event the arms aredetermined to be alternating and the head is determined to be orientatedhead-down. A crawl stroke is determined in Block 236 in the event thearms are determined to be alternating and the head is determined to beorientated head-up. These identifications allow the present invention toidentify the crawl and back stroke swimming strokes from accelerometerdata sensed by the arm sensors 92 and head sensors 14. Alternatively, asimilar determination can be made either using the arms sensors 92and/or head sensors 14 exclusively by quantifying the subtle nuances ofeach stroke. The illustrated method was described in more detail as thismethod is believed to provide a more easily detectable and/or repeatableprocess for crawl and back stroke identification.

An analysis of the swimmer's arm angle may be made in Block 240 toidentify performance of one of the butterfly and breast strokes inBlocks 242, 244 in the event the arms are determined to be moving inunison, making it impossible for the swimmer to be performing a propercrawl or back stroke (the present invention assumes legal or properstrokes are being performed in most cases at least in so far asidentifying the stroke with one of the known crawl, back, butterfly, andbreast strokes). The hands tend to move to a vertical position during abreast stroke's sculling motion and to remain in a flat position duringa butterfly stroke. This results in a different average angle of the armthat can be registered with the accelerometers measuring gravity(gravity appears as the DC average from the accelerometer).

The arm angle may be found by measuring the DC offset in the X- andY-axis over the entire lap length, i.e., from end to end of the pool.The DC offset is considered to be the zero frequency point of thefrequency varying accelerometer data, which may be determined byaveraging each of the x and y values for a given lap to determining aresulting x and y DC offset value. An arm angle value B_(a) may beidentified therefrom to represent the X DC offset value minus the Y DCoffset value. If the arm angle value B_(a) is above a threshold, thestroke type is butterfly; otherwise the stroke type is breaststroke. Thethreshold generally corresponds with the butterfly stroke beingidentified when the value B_(a) is small and the breast stroke beingidentified when the value B_(a) is large, mainly due to the y valuetypically being smaller during the breast stroke.

The B_(a) value used to arbitrate between the butterfly and breaststroke may be determined according to the following formula:B _(a)=avg(x _(R))−avg(y _(R))

wherein x_(R) and y_(R) are values used by the right arm sensor toquantify movement in the corresponding x and y directions. The X_(L) andY_(L) values are omitted to simply process, however, those values may beconsidered to confirm the results of the right arm sensor. The z valuesare shown to be omitted because the z values tend not to reliablerepresent and/or add value to the desired determination. The head sensordata may also be used to identify breast and butterfly swimming strokes,as described below in more detail.

FIG. 12 illustrates a flowchart 250 for stroke identification where amethod is described for determining whether a swimmer is performing acrawl stroke, back stroke, butterfly stroke, or breast stroke based onthe accelerometer data captured solely from the head sensor 14. Themethod of FIG. 12 may be beneficial, for example, when the limb devices62 are unable to reliably communication with the master station 16 dueto being underwater, such as when performing the breast and butterflystrokes. The ability to rely on the head sensor 14 to identify stroketype also allows for information to be output to the coach's tablet orfor other uses in real-time while the swimmer is training andinformation is unavailable form the arm and/or leg sensors 62. Themaster station 16 can then rely on the identified stroke to facilitatemetric feedback in the absence of information being received from thelimb devices.

Once the limb devices 62 are able to communicate with the master station16 again, such as when the limb devices 62 are out of the water for asufficient period of time, the collected data may then be transmitted tothe master station 16 to confirm the stroke identification or to performother refinements to the data known to the master station 16. The methodof FIG. 12 is described with respect to the processor of the head device14 performing the noted stroke discriminations, optionally in additionto the master station 16. The head sensor 14 may then rely on the strokeidentification in subsequent performance metric calculations, asdescribed below in more detail where filters and other processingparameters can be set at least based in part on the identified swimmingstroke. The ability of the swim devices 14, 62 to identify the swimstroke independently of the master station 16 can be beneficial infacilitating the stroke identification dependent processes andcalculations without having to be notified by the master station of thestroke being performed, which may otherwise be problematic if the swimdevice 14, 62 is periodically underwater or otherwise unable tosufficiently communicate with the master station 16 (leg and arm sensors62 may be able to perform related processing).

The stroke identification begins in Block 252 where an assessment ismade regarding the head being orientated head-up or head-down, such asby using the processing described above. The back stroke is identifiedin Block 254 if the orientation is head-down and further assessments arebegun in Block 256 in the event the head orientation is head-up. Block258 relates to an initial assessment where y versus z activity of thehead device is analyzed. The y versus z activity can be used to identifythe crawl stroke in Block 260 or the need for still further processingin Block 262. The y versus z activity reflects a swimmer's head tendingto roll more noticeably from side to side when breathing whileperforming a crawl stroke than when performing one of the breast andbutterfly strokes.

The side to side movement indicative of the crawl stroke may bedetermined in the event a value C representative of the amount of ymovement relative to x and z movement is sufficiently high. The H valuemay be determined by

1. Removing the DC offset from X-, Y-, and Z-axes with a highpassfilter; finding a sum of squares point-by-point in each axis;

2. Measuring Y-axis RMS amplitude;

3. Adding X- and Z-axes (squared) to find combined RMS amplitude; and

4. Dividing Y-axis RMS by X- and Z-axis combination RMS amplitude suchthat an C greater than a threshold can indicate crawl stroke detectedand an C value less than threshold can indicate breaststroke orbutterfly detection. The threshold may be determined empirically basedon a swimmer database used to reference various swimming patterns.

The C value used to arbitrate the crawl stroke may be determinedaccording to the following formula:C=y _(H)/(x _(H) +z _(H));

wherein x_(x), y_(H), and z_(H) are values used by the head sensor toquantify movement in the corresponding x, y and z directions. (Note thisformula, and other formulas included herein, omit the RMS, squaring,and/or filtering for simplicity as those operations may be optionallyused to reduce signal-to-noise issues. The contemplated processes may beformed from the raw data.)

An assessment of the pattern of the swimming stroke is made in Block 262in the event the head roll is insufficient to verify occurrence of thebreast stroke or butterfly stroke in Blocks 264, 266. Block 262 relatesto performing a best-fit correlation where the accelerometer datacollected by the head device 14 is patterned for comparison to knownstroke patterns. Since butterfly and breast strokes exhibit similar headmotion to discriminate between the two, a representative set ofcorrelation filters, some butterfly and some breaststroke, may becreated for comparison purposes. The known stroke patterns may beretrieved from a memory of the swim device 14 and/or master station 16based on accumulated historical performances of the current user, fromsurveys conducted for a plurality of swimmers, i.e., a template can beformed based on patterns established by multiple swimmers, or throughsome other suitable process. The process may be performed as part of acorrelation where each of the known patterns is used to filter theaccelerometer collected data. The stroke associated with the filtergenerating the greatest amplitude peaks may be determined to be thestroke being performed by the swimmer, i.e., the best-fitting filterwill provide the greatest amplitude peaks.

The best-fit filtering process may include:

1. Identifying each stroke period (zero-crossings in X-axis filtereddata averaged over the lap length) to identify the dominate strokeperiod for the accelerometer data of one of the arm sensors (the processmay include both arm sensors if desired);

2. Creating a test filter from the two stroke periods (could use more orless than two) centered about the lap's center most stroke (the testfilter being the sampled accelerometer data. (FIG. 13 illustrates anexemplary test filter selected from a graph of accelerometer data.)

3. Filtering the accelerometer data with the test filter to find signalpeaks. (FIG. 13 illustrates a correlated signal graph where theresulting peaks are evident.)

4. Identifying an average stroke period based on each stroke periodshown within the correlated signal graph generated in step 3.

5. Selecting a filter family from a plurality of filter families havingstroke periods most closely aligned with the average stroke period foundin step 4. The filter families include know patterns for breast andbutterfly strokes organized according to stroke period (stroke periodmay vary depending on swimmer competence, size, etc.)

6. Filtering the accelerometer data with each a plurality of filtersinclude within the selected filter family to identify filter producingthe highest amplitude output;

7. Identifying the strokes to be butterfly strokes if the highest outputwas from a butterfly stroke filter and to be breast strokes if thehighest output was from a breast stroke filter.

Returning to FIG. 9, a stroke counting operation may be performed inBlock 270 according to the stroke identified in Block 208. Block 270 isshown to occur after Block 208 for exemplary purposes and withoutintending to limit the present invention to any particular sequence ofoperations. Some or all of the processing contemplated by the presentinvention each Block 270 may be performed in parallel such that the swimdevice processors and/or the master station processor(s) may be operableto execute the contemplated process in parallel or any other desiredsequence. One stroke counting process contemplated by the presentinvention relates to an autocorrelation or cross-correlation filteringprocess where a reference stroke is taken from the accelerometer dataand compared to rest of the accelerometer data to count the number ofstrokes. This reference stroke may be determined on a lap-by-lap basis,at periodic/random intervals while the swimmer continues the samestroke, and/or once for the identified stroke.

FIG. 13 illustrates a graph 272 of accelerometer data collected from oneof the swim devices over a particular period of time. Correlationfiltering is a mathematical process of comparing a length of data 272 toa reference waveform 274, which may be selected from the data 272 orknown in advance, (i.e. passing it across a length of data) in search ofa match(s). Where the data waveform 272 closely matches the referencewaveform 274, the mathematical equation of the correlation filteringproduces a filtered signal 276 high output signal amplitude (signalpeak), i.e., significant rate changes with improved signal-to-noiseratio, at the point in time where the match occurs. These peaks can bereliably identified by a peak detection- and threshold-based algorithm.

Autocorrelation is a special correlation case where a reference waveformis extracted from the actual signal. This process for counting strokesover a single swimming lap may include:

1. Deciding when the swimmer is producing strokes (bracketing a lengthof data to be processed).

2. Establishing a dominant period, or rate, of the stroke within thelength (repeating cycle of activity likely to represent a stroke). Theaverage stroke period (or dominant stroke period) may be found by lowpass filtering the axis with the highest RMS amplitude and computing theaverage period using zero-crossings.

3. Identifying and recording a preliminary reference stroke (e.g., thestroke used for the autocorrelation, such as a fixed stroke, i.e. asecond stroke (first and last strokes are typically uncharacteristic) ormiddle stroke occurring at the middle of the lap, or the stroke patternmost commonly occurring throughout the lap). A preliminary referencestroke signal may be selected to be twice the average period near amiddle of the swum length centered on the peak of a signal computed as(X²+Y²+Z²). Unlike the dominate period that represents time, thepreliminary reference stroked is a sample of the actual signaling andincludes values to reflect movement in each of the x, y, and z axes.

4. Positioning the start of the preliminary reference stroke to coincidewith the initiation of the first stroke. The positioning is a centeringof the reference stroke by isolating the region where the first strokebegins and then applying an activity-detection filter over this regionto identify the transition point from no activity to activity. Thepositioning of the reference stroke aligns the reference stroke phasewith the data length (accelerometer data for one or more laps) phase.This alignment influences the filtering in step 5 since the peaks of thestrokes detected thereafter in the data length will be aligned relativeto the aligning of the reference stroke to the first stroke.Misalignment can result in extra strokes being counted or stokes beingomitted.

5. Filtering the data length with the reference stroke to identifypeaks, and therefrom, the corresponding strokes.

6. Identifying and averaging three strokes identified in step 5 tocreate a final reference stroke (i.e., after all the strokes areidentified with the preliminary reference stroke one of more of thosestrokes—noted as three—may be used to generate another, final referencestroke). Averaging three strokes over the length makes the correlationprocess more forgiving of variations in the swimmers stroke. The second,second to last, and centermost strokes may be selected, optionally usingonly strokes greater than 30% of the peak correlation.

7. Correlating the data length against the final reference stroke tofind all strokes. The correlation output signal may be scaled relativeto the highest peak across the length from 0 to 100%.

8. Counting strokes as those having outputs above a numerical threshold,e.g., 30%.

FIG. 13 illustrates the final reference stroke 274 as selected from thegraph of accelerometer data. This final reference stroke 274 is shown toinclude data for each of the x, y, and z axes, however, the presentinvention fully contemplates relying on less than all of the axes datawhen counting strokes. The correlated output of the final referencestroke 274 as filtered through the graphed data is shown with acorrelated output 276. The correlated output 276 includes a number ofmore clearly defined peaks. The amplitude of each peak quantifies howclosely the corresponding segment of the accelerometer data matched withthe final reference stroke. The present invention counts a stroke foreach one of the amplitudes having a value greater than a stroke countingthreshold. The stroke counting threshold may vary from swimmer toswimmer, particularly with respect to swimmers having less skill or morevariability in their stroke patterns. Based on empirical analysis, thethreshold is set to 35% of the maximum peak amplitude for the data set.

FIG. 14 illustrates a histogram plot 280 of the number of amplitudesdetected after completion of an autocorrelation process. The y-axis isplotted according to counts representative of sampling intervals orperiods during which an amplitude was identified for a certain stroke.The number of counts is rather large to represent a certain samplingsize. A similar approach may be used with a smaller sampling size, suchas on per swimmer basis, lap or multiple-lap basis, etc. The x-axis isplotted according to the output value of the corresponding amplitudesmeasured as a percentage from 100%, i.e., closer matches having agreater percentage than lower matches. The graph 280 illustrates apartially exponential trend where the number of counts having amplitudesgreater than 80% start to rapidly increase. This point of inflection,commonly referred to as a knee, may be used to signify ‘quality’ or‘matched’ strokes. The matched strokes may be considered to be thestrokes most closely matching the dominate stroke (reference stroke0 ofthe swimmer, at least for the measured lap(s).

The ability to contrast matched strokes with other counted strokesallows the present invention to further refine the performance metricsto include an assessment of how well the swimmer was able to repeat thesame stroke throughout the lap. The consistency and ability of swimmersto repeat the same stroke throughout a given lap can be beneficial inallowing the swimmer to accurately assess their performance. FIG. 15illustrates a matching plot 282 that may be generated to pictoriallyreflect how well the swimmer was able to repeat the same strokethroughout one or more laps. The plot includes a first, second, andthird threshold (represented respectively at 75%, 50%, and 35% lines)where the first threshold is selected to correspond with the matchpercentage occurring at the histogram knee of FIG. 14 (e.g., where thetop ⅔ of the amplitude strokes occur, which is selected as 75% in theillustrated example), the second threshold is selected to correspondwith a minimum match percentage, and the third threshold is selected tocorrespond with acceptable match percentages for the first and laststroke, which typically and acceptable differ from the other strokes(e.g., 50%).

The matching plot includes an x to mark each of the stroke amplitudesregistered while swimming a lap (only one lap is shown however anynumber of laps may be included end-to-end and/or one lap may beillustrated with the data from multiple laps being superimposed).Matched or the highest quality strokes may be those occurring above thefirst strokes, irregular or lower quality strokes may be those occurringbetween the first and second threshold, and invalid strokes may bedisregarded as being too low quality since strokes below 25% correlationoutput may be incomplete or more representative of another non-strokeaction. The matching plot allows the swimmer and/or coach to review theswimmers stroke matching throughout the lap and to easily identifyperiods during which the swimmer may have deviated from the referencestroke. This can be beneficially in identifying trends and habits forthe swimmer, such as whether the swimmer tends to perform an irregularstroke at a particular portion of the lap, for example, if the swimmerdevelops a habit of shorting a middle stroke.

Another stroke counting process of the present invention contemplatescounting strokes using accelerometer data from only the x and z axes.This stroke counting process may be beneficial for use with the headdevice and counting strokes during a breast stroke when the transmissionof accelerometer data from the arm and leg sensors can be moredifficult. The stroke counting may be generally similar to thatdescribed above in at least so far as correlating the captured data withitself to identify peaks. The counting may be further refined bylimiting the correlation to the x and y values, and optionally withoutaveraging multiple strokes to identify the final reference stroke, i.e.,one reference stroke can be selected and used throughout. In the case ofa breast stroke the stroke counting process may vary slightly in thatthe reference stroke may be aligned relative to a beginning of thebreast stroke, such as at a rising edge of the x-axis data, whichtypically occurs during breast stroke when the swimmer's head rises upout of the water.

Returning to FIG. 9, an additional performance metric may be determinedin Block 290 to reflect the number of kicks performed while swimming oneor more laps. The number of kicks may be counted from accelerometer dataof the leg devices 94, and in some cases, simply from the accelerometerdata of the arm/head device(s) 92, 14, e.g., when performing the breaststroke the number of kicks can be counted based on the number of strokessince the swimmer makes one kick for each properly performed breaststroke. FIG. 16 illustrate a graph 292 of leg accelerometer datafiltered according to a kick counting filtering process contemplated bythe present invention. FIG. 17 illustrates a flowchart 294 of the kickcounting process used to generate the graph and assess the number ofexecuted kicks.

Block 296 relates to assessing RMS amplitude of peaks detected by eachof a plurality of filters applied to the leg data. As shown in FIG. 16,a number of lowpass filters (LPF 1-7) are used to filter the legaccelerometer data in the frequency domain. Each filter reflects thefrequency characteristics of the accelerometer data relative to thefrequency characteristics of each of the applied filters. The filtersselected for Block 296 may vary depending on personal characteristics ofthe swimmer, i.e., filters may be selected passed on past history orskill level, the filters may be the same for each swimmer with each ofthe filters being aimed to identify a unique pattern (the patterns maybe generated empirically), or through other means.

FIG. 18 illustrates a graph 300 raw, y axis data for a sinusoidalkicking pattern. FIG. 19 illustrates a graph 302 raw, y axis data for animpulse kicking pattern. The sinusoidal kicking pattern tends to have aless degree of amplitude variation relative to the amplitude variationof the impulse kicks, typically due to the impulse kicks being more amore of a ‘snapping’ kick as opposed to the sinusoid shape associatedwith a ‘rolling’ kick. Block 304 relates to measuring the variance ofthe RMS results for all the applied filters, i.e. LPF 1-7. If thevariance is determined in Block 306 to be too low, (i.e. all filtersgive similar RMS output) and no single filter result stands out,impulse-type kicks (“snappy” kicks) are assumed. A high-pass filter isselected in Block 308 to filter the accelerometer data for peaks in theevent the variance is sufficiently great. The filter of the previouslyapplied filters producing the highest RMS output (measured as an averagefor each peak and/or as the one generating the single greatest peak) isselected in Block 310 in the event the variance is less than thethreshold.

FIG. 20 illustrates a graph 312 the values of FIG. 18 being filteredwhere each peak is identified as a single kick. Block 314 counts kicksto occur at each peak. The counted kicks may further be defined withinthe generated performance metrics such that kicks are defined as peaksgreater than 10 (empirically derived to represent kicks) in the legY-axis acceleration data while the arms are considered to be stroking(between the first and last stroke). The total kick count for breaststroke and butterfly stroke is the kick count of the limb with thegreatest number of kicks. The total kick count for back stroke and crawlstroke is the sum of the kick count of each limb. Kick tempo is theaverage time taken per kick over a length swam. Kick tempo is computedby dividing the kick duration by the kick count. The kick duration isdefined as the as the time difference between the last kick and thefirst kick within the counting region.

The foregoing description, and in particular the assessments maderegarding stroke identification, stroke counting, and kick counting,presume the data used in those assessment corresponded with a period oftime suitable to undertaking the related processing, i.e., that theswimmer was actually swimming and not walking around or within the poolor performing some other non-swimming activity. The time markers used toassess a swum lap and when to begin/stop counting strokes were alsopresumed to be known. The processes of actually detecting whether theuser is actually swimming and whether has started/completed a swum lapcan be determined from the accelerometer data in accordance with thepresent invention and as further described below.

The initial determination of whether the swimmer is actually swimmingmay be based on an end of length (EOL) determination. The EOL may be abroad time bracket intended to identify conditions typically associatedwith a swimming based event, i.e., events representative of a swimmeractually performing legal/proper swimming strokes. A starting EOL and anending EOL may be determined to identify when a user starts and stopsswimming. The EOLs can be helpful in limiting processing demands on theswim devices 14, 62 and/or master station 16 to data collected aroundthe time of the EOLs, i.e., some processing may be saved by limiting theprocessed data occurring within some period of time prior to the staringEOL, data occurring within the period of time between the starting andending EOLs, and data occurring within some period of time after theending EOL. This process of selecting or segmented the collectedaccelerometer data in one or more portions as a function of the EOLs maybe characterized as bracketing. The bracketing may define brackets intime during which particular portions of the data are analyzed togenerate one or more corresponding performance metrics.

Bracketing a length of swimming activity enables the present inventionto process metrics on a length by length basis or at virtually anydesirable interval. Length bracketing (EOL selection) may beaccomplished by band-pass filtering the arm accelerometer output for aspecific range of acceleration as typically observed at the beginningand end of each length. The corresponding x, y, and z values may besquared to accentuate gross changes in speed of the swimmer and summedto identify amplitude peaks. The order and sequence of peaks may be thenbe used to identify patterns/sequences indicative of EOL events. Whilethis broad approached to identify periods during which the swimmer isactually swimming can be helpful in limiting processing demands, thepresent invention contemplates a more granular and precise process foridentifying markers indicative of when the swimmer starts and completeseach lap, e.g., markers used to quantify time and other metric outputsrequiring more precision.

The point at which the swimmer starts and completes each lap can bedifficult to ascertain from the accelerometer data since some motionsand movement performed while swimming can appear from the accelerometerdata to be almost identical to events occurring at the beginning andending of a lap, i.e., events associated with the swimmer turning andpushing off the wall to start another lap (referred to as a middle lap),touching the wall when completing a last lap, and diving into a pool tostart a first lap (diving may be considered to be equivalent to apush-off event). Bracketing can be used to limit the portions of theaccelerometer data used to assess lap events (referred to asstarting/ending timestamps and/or split times) and to generateperformance metrics as a function thereof. The lap events may bedetermined when a head event is followed by a push-off event. The headevent represents a typical head movement performed by the swimmer priorto pushing off the wall for another lap and the push-off eventrepresents events typically occurring when the swimmer pushes of thewall.

FIG. 21 illustrates a schematic 324 useful in understanding thedetermination of the lap events. The determination may be based on ananalysis of previously collected accelerometer data is reviewed afterdetecting initiation of a first stroke (detectable according to theprocesses noted above). A bracketed search region defines a priorportion of the accelerometer data being analyzed relative to the firststroke. Within that portion, push-off search regions are set from eachidentified head event (defined below in more detail). The push-offsearch regions are then analyzed for occurred of push-off events(defined below in more detail). The illustrated example includes a pairof push-off events occurring within a first bracket search region and asingle push-off event occurring within a second bracket search region.One of these push-off events is then determined to mark the end/start ofthe swum lap.

The head event may include a head dip, a head throw, and a head roll.The head dip may be characterized as a motion where the swimmer's headexperiences a rapid transition from face down (or up) to face up (ordown) during a flip turn near the end of length for freestyle and backstrokes. (The head dip is usually coincidental with the swimmers feetcoming out of the water during a flip turn.) The head throw refers tothe turning motion of head following a wall touch in breast stroke andbutterfly. The head roll refers to the turning of the head during therollover of the swimmer in back stroke.

The head throw event is generated by low-pass-filtering Y axisacceleration data (from the head device), squaring it (eliminatenegative numbers), and finding peaks above a dynamic threshold. Thedynamic threshold may be generated by low-pass-filtering the squared Yaxis data to eliminate false head throw events from freestyle breathing.The head throw can be falsely determined in the event the swimmercommits any of the following: aggressive side to side breathing infreestyle; aggressive side breathing in butterfly; and aggressive headturning at the wall prior to pushing off the wall, which highlights theneed to bracket head event detections relative to the other event (e.g.,push-off event) representative of the swimmer actually make a turn. Thehead roll event may be found by finding the minimum in thelow-pass-filtered Z axis within four seconds prior to each head dip,given that the minimum is below a threshold and returns to a level abovea threshold within the 4-second window. The head dip may be generated bylow-pass-filtering X and Z axis acceleration data (from the headdevice), subtracting Z from X, and finding a peak above 0.28G.(Threshold setting of 0.28G is empirically derived from analysis of theswimmer database to be suitably reliable head dip detection threshold.)

The push-off event used to confirm a preceding head event beingrepresentative of a turn may be when the swimmers feet leave the walland head experiences an abrupt change in velocity (acceleration).Push-off events may be generated by high-pass-filtering X and Z axisdata of the head accelerometer data (limb data could also be used),subtracting Z axis from X, and finding peaks above 1.2G (thresholdsetting of 1.2G is empirically derived from the swimmer database suchthat the most reliable push-off detection is achieved).

The table below list design parameters selected to facilitate furtherperformance metrics contemplated by the present invention and are notintended to limit the scope and contemplation of the present invention.

FLIP_TURN_SEARCH_FORWARD 2 secondsFLIP_TURN_SEARCH_BACKWARD_PERIOD_PERCENT 200%ROLLOVER_HEAD_DIP_SEARCH_FORWARD 3 secondsROLLOVER_SEARCH_FORWARD_PERIOD_PERCENT  50%BACK_REVERSE_FLIP_OR_TOUCH_TURN_PERIOD_PERCENT 125%BACK_REVERSE_FLIP_OR_TOUCH_TURN_SERACH_FORWARD 3 secondsTOUCH_TURN_SEARCH_FORWARD 4 seconds TURN_TO_PUSH_OFF_SEARCH_FORWARD 2seconds FIRST_STROKE_PUSH_OFF_SEARCH_REGION_OFFSET 1 secondPREVIOUS_LENGTH_SPLIT_END_TIME_OFFSET 0 seconds

A turn may be used to classify the current length as a first length,mid-length, or last length. This length type classification may becritical for proper length time measurement. Supported turns may includeflip turns, backstroke rollover turns, backstroke reverse flip turns,backstroke touch turns (backstroke to breaststroke/freestyle/butterflytransition), and touch turns (two hand touch or single hand touch). If aturn is not found at the end of a length, it may be assumed that theswimmer is finishing a swim and the length will be classified as a lastlength. If a turn is not found at the beginning of a length, it may beassumed that the swimmer is starting a swim and the length will beclassified as either a first length or a single length (see definitionsbelow).

A flip turn may be defined as any head dip event generated within a headdip search region. The head dip search region may be a time windowdefined from the last detected stroke timestamp minusFLIP_TURN_SEARCH_BACKWARD_PERIOD_PERCENT of the average stroke period(counted as matched and irregular strokes) to the last detected stroketimestamp plus FLIP_TURN_SEARCH_FORWARD seconds. A back stroke rolloverturn may be defined as any head roll event generated within the headroll search region. The head roll search region is a time window definedfrom the last detected stroke timestamp minus two times the averagestroke period to the last detected stroke timestamp plusROLLOVER_SEARCH_FORWARD_PERIOD_PERCENT of the average stroke period. Ahead dip event must follow the head roll event within aROLLOVER_HEAD_DIP_SEARCH_FORWARD second window. A backstroke reverseflip turn may be defined as any head dip event generated within thereverse flip turn search region. The reverse flip turn search region isa time window defined from the last detected stroke timestamp minusBACK_REVERSE_FLIP_OR_TOUCH_TURN_PERIOD_PERCENT of the average strokeperiod to the last detected stroke timestamp plusBACK_REVERSE_FLIP_OR_TOUCH_TURN_SEARCH_FORWARD seconds. A backstroketouch turn may be defined as any head throw event generated within theback touch turn search region. The back touch turn search region is atime window defined from the last detected stroke timestamp minusBACK_REVERSE_FLIP_OR_TOUCH_TURN_PERIOD_PERCENT of the average strokeperiod to the last detected stroke timestamp plusBACK_REVERSE_FLIP_OR_TOUCH_TURN_SEARCH_FORWARD seconds. A touch turn maybe defined as any head throw event generated within the head throwsearch region. The head throw search region is a time window definedfrom the last detected stroke timestamp to the last detected stroketimestamp plus TOUCH_TURN_SEARCH_FORWARD seconds.

A first length is defined as a length that begins without a turn andends with a turn. The length time during a first length is measured asthe difference between the split start timestamp and the split endtimestamp. The split start timestamp is the timestamp of the startingpush-off of the current length while the split end timestamp is thestarting push-off of the next length. The split start time for a firstlength is found through a search process. Bracketing the push-off eventhelps in searching for and finding the correct push-off event. Head dipevents, and head throw events within the push-off search region are usedas bracketing events with priority given to head throw events.

The bracket search region starting timestamp may be the minimum ofeither:

1. MAX_BREAKOUT_TIME seconds prior to the initiation of the first strokeor

2. The previous length's split end time plusPREVIOUS_LENGTH_SPLIT_END_TIME_OFFSET

The bracket search region ending timestamp is the initiation of thefirst detected stroke minus FIRST_STROKE_PUSH_OFF_SEARCH_REGION_OFFSETseconds.

The push-off search region starting timestamp is defined as the bracketevent timestamp. The push off search region ending timestamp isTURN_TO_PUSH_OFF_SEARCH_FORWARD seconds beyond the bracket eventtimestamp.

The following guidelines may be used to identify the push-off event tobe used in marking starting/ending times and calculating performancemetrics:

1. If only one push-off event in total is found for all push-off searchregions, then that push-off event is used.

2. If multiple push-off search regions include at least one push-offevent, the push-off event within the push-off search region having themaximum amplitude push-off event is used as the split start time. If themaximum amplitude push-off event is the same for two or more push-offsearch regions, then the push-off event within the earliest pair is usedas the split start time.

3. If no push-off search region includes a push-off event, then themaximum amplitude push-off event within the bracketed search region isused as the split start time.

4. If multiple push-off events exist with the same amplitude outside ofeach push-off event search regions, the earliest push-off event is usedas the split start time.

5. If no push-off events exist within the bracketed search region, theearliest arm EOL event (earliest activity detected by the SD) is used asthe split start time.

A mid-length is defined as a length that begins and ends with a turn.The length time during a mid-length is measured as the differencebetween the split start timestamp and the split end timestamp. The splitstart timestamp is the timestamp of the starting push-off of the currentlength while the split end timestamp is the starting push-off of thenext length. For mid-lengths, the split end time of the previous lengthis used as the split start time of the current length.

A last length is defined as a length that starts with a turn and endswith a finish. The length time during a last length is measured as thetime difference between the split start time and the split end time. Thesplit start time is the timestamp of the starting push-off of thecurrent length and the split end time is the timestamp of the lastdetected stroke plus 50% of the average stroke period. The split endtime is the timestamp of the last detected stroke (irregular or matched)plus 50% of the average stroke period to account for a wall touch. If athe final stroke of the length finished coincidentally with a walltouch, the last length split time will be match the traditionaldefinition of split time. Optionally, wireless wall touch sensors may beused to further refine the wall touch/turn calculations.

A single length is defined as a length that starts without a turn andends with a finish. The length time during a single length is measuredas the time difference between the split start time and the split endtime. The split start time is the timestamp of the starting push-off ofthe current length and the split end time is the timestamp of the lastdetected stroke plus 50% of the average stroke period.

A touch turn at the end of a breaststroke and butterfly length triggersthe system to classify the length as a mid-length. If the swimmer swimsa butterfly or breaststroke last length and then pushes off to perform afirst length within TOUCH_TURN_SEARCH_FORWARD window of the previouslengths last detected stroke (matched or partial), the system willassume the previous length was a mid-length and apply the mid-lengthsplit time definition.

Tempo is the average time taken per stroke over a length swam. Tempo iscalculated by dividing stroke duration by number of strokes taken. Forbreaststroke lengths, stroke duration and stroke count are computedbased on the head stroke events. The stroke duration is defined as theas the time difference between the timestamp of the last detected matchstroke plus 50% of the average stroke period and the first detectedmatch stroke minus 50% of the average stroke period. The number ofstrokes taken between first and last matched stroke is the sum ofmatched and irregular strokes and does not include any non-strokeevents.

For butterfly lengths, stroke duration and stroke count are computedseparately for each limb. The stroke duration and stroke count of eachlimb is used to compute a tempo for each limb. The tempo of each limb isthen averaged together resulting in a final tempo measurement. Thestroke duration is defined as the as the time difference between thetimestamp of the last detected match stroke plus 50% of the averagestroke period and the first detected match stroke minus 50% of theaverage stroke period. The number of strokes taken between first andlast matched stroke is the sum of matched and irregular strokes and doesnot include any non-stroke events.

For freestyle and backstroke lengths, stroke duration and stroke countare computed after combining the limb stroke events together. The strokeduration and stroke count is then used to compute a final tempomeasurement. The stroke duration is defined as the as the timedifference between the timestamp of the last detected match stroke plus50% of the average stroke period and the first detected match strokeminus 50% of the average stroke period. The number of strokes takenbetween first and last matched stroke is the sum of matched andirregular strokes and does not include any non-stroke events.

Average Distance Per Stroke (DPS) is the length of the pool divided bynumber of matched plus irregular strokes taken between two consecutivepush off markers. Pace is the average speed over a given length. Pace iscalculated as the length of the pool divided by the length timemeasurement.

As required, detailed embodiments of the present invention are disclosedherein; however, it is to be understood that the disclosed embodimentsare merely exemplary of the invention that may be embodied in variousand alternative forms. The figures are not necessarily to scale, somefeatures may be exaggerated or minimized to show details of particularcomponents. Therefore, specific structural and functional detailsdisclosed herein are not to be interpreted as limiting, but merely as arepresentative basis for the claims and/or as a representative basis forteaching one skilled in the art to variously employ the presentinvention. The features of various implementing embodiments may becombined to form further embodiments of the invention.

What is claimed is:
 1. A system for generating performance metrics for auser comprising: at least two arm sensors operable to sense user armmovement in an x, y, and z direction; at least one head sensor operableto sense user head movement in an x, y, and z direction; at least oneleg sensor operable to sense user leg movement in at least a ydirection; at least one processor operable to determine performancemetrics for the user based on the movement sensed by the arm, leg andhead sensors; wherein at least one performance metric determined by atleast one of the at least one processor identifies a number of leg kicksperformed by the user between a starting marker and an ending markerbased on y values used by the at least one leg sensors to quantifymovement in the y direction.
 2. The system of claim 1 wherein at leastone performance metric determined by at least one of the at least oneprocessor identifies whether left and right arms of the user areperforming an alternating swimming stroke based on movement sensed whileone of the at least two arm sensors senses user movement of the left arm(referred to as a left arm sensor) and one of the at least two armsensors senses user movement of the right arm (referred to as a rightarm sensor).
 3. The system of claim 2 wherein the alternating typeswimming stroke is determined in the event a value A is greater thanzero, wherein A is determined according to the following formula:$A = {{§\left( {\sum\limits_{i = 1}^{N}{X_{Ri}X_{Li}}} \right)}^{2} + {§\left( {\sum\limits_{i = 1}^{N}{Y_{Ri}Y_{Li}}} \right)}^{2} + {§\left( {\sum\limits_{i = 1}^{N}{Z_{Ri}Z_{Li}}} \right)}^{2}}$wherein x_(L), y_(L), and z_(L) are values used by the left arm sensorto quantify movement in the corresponding x, y, and z directions;wherein x_(R), y_(R), and z_(R) are values used by the right arm sensorto quantify movement in the corresponding x, y, and z directions; andwherein § is a positive value of 1 when the corresponding x, y, and zvalue represents movement the positive direction and a negative value of1 when the corresponding x, y, and z value represents movement in thenegative direction.
 4. The system of claim 3 wherein the alternatingswimming stroke is determined by using movement sensed by no more thanthe left and right arm sensors.
 5. The system of claim 1 wherein atleast one performance metric determined by at least one of the at leastone processors identifies whether the head of the user is orientated toreflect performing one of a head-up type and a head-down type swimmingstroke based on movement sensed by the at least one head sensor.
 6. Thesystem of claim 5 wherein the head-up type swimming stroke is determinedin the event a value H is greater than a threshold and the head-downtype swimming stoke is determined in the event the value H is less thanthe threshold, wherein H is determined according to the followingformula:H=X _(H) +Z _(H); wherein x_(H) and z_(H) are values used by the headsensor to quantify movement in the corresponding x and z directions. 7.The system of claim 5 wherein the heads-up swimming stroke is determinedby using movement sensed by no more than the at least one head sensor.8. The system of claim 1 wherein at least one performance metricdetermined by at least one of the at least one processor identifieswhether left and right arms of the user are performing one of a breaststroke and a butterfly swimming stroke based on movement sensed whileone of the at least two arm sensors senses user movement of the left arm(referred to as a left arm sensor) and one of the at least two armsensors senses user movement of the right arm (referred to as a rightarm sensor).
 9. The system of claim 8 wherein the breast stroke isdetermined in the event a value B_(a) is less than a threshold and thebutterfly swimming stoke is determined in the event the value B_(a) isgreater than the threshold, wherein B_(a) is determined according to thefollowing formula:B _(a)=avg(x _(L) +x _(R))−avg(y _(L) ++y _(R)) wherein x_(L) and y_(L)are values used by the left arm sensor to quantify movement in thecorresponding x and y directions; and wherein x_(R) and y_(R) are valuesused by the right arm sensor to quantify movement in the corresponding xand y directions.
 10. The system of claim 1 wherein at least oneperformance metric determined by at least one of the at least oneprocessors identifies whether the head of the user is orientated toreflect performing a crawl type swimming stroke based on movement sensedby the at least one head sensor.
 11. The system of claim 10 wherein thecrawl swimming stroke is determined in the event a value C is greaterthan a threshold and not determined in the event the value C is lessthan the threshold, wherein C is determined according to the followingformula:C=y _(H)/(x _(H) +z _(H)); wherein x_(H), y_(H), and z_(H) are valuesused by the head sensor to quantify movement in the corresponding x, yand z directions.
 12. The system of claim 1 wherein at least oneperformance metric determined by at least one of the at least oneprocessor identifies whether left and right arms of the user areperforming one of a breast stroke and a butterfly swimming stroke basedon movement sensed by the at least one head sensor.
 13. The system ofclaim 12 wherein: the breast stroke is determined in the event abest-fit of x_(H), z_(H), and z_(H) values used by the head sensor toquantify movement in the corresponding x, y and z directions is with oneof a plurality of breast stroke correlation filters; and the butterflystroke is determined in the event a best-fit of x_(H), z_(H), and z_(H)values used by the head sensor to quantify movement in the correspondingx, y and z directions is with one of a plurality butterfly strokecorrelation filters.
 14. The system of claim 1 further comprisingwherein at least one performance metric determined by at least one ofthe at least one processor identifies a number of swimming strokesperformed by the user between a starting marker and an ending markerbased on movement sensed by at least one of the at least two arm sensorsand the at least one head sensor.
 15. The system of claim 14 wherein thenumber of swimming strokes is determined by correlating x, y, and zvalues sensed while the user swims between the starting and endingmarkers with a reference stroke selected from the x, y and z values, thex, y, and z values being used by the arm sensors to quantify movement inthe corresponding x, y, and z directions.
 16. The system of claim 15wherein the correlating of the x, y, and z values with the referencestroke includes filtering the x, y, and z values with the referencestroke to identify amplitude peaks, wherein each amplitude peakcorrelating with at least a P percentage of the reference stroke iscounted as one of the number of swimming strokes.
 17. The system ofclaim 16 wherein the P percentage is at least one of 35% and a valuecorresponding with a lowest amplitude peak within the greatest ⅔ of theamplitude peaks.
 18. The system of claim 17 further comprising at leastone of the at least processors characterizing each of the number ofswimming strokes as one of matched and irregular, the matched strokeshaving amplitude peaks of greater than 79% and each irregular strokehaving amplitude peaks of between 79% and 35%.
 19. The system of claim16 further comprising characterizing a first stroke occurring proximatethe starting marker to be a matched stroke in the event thecorresponding amplitude peak is at least 50% and a last stroke occurringproximate the ending marker to be a matched stroke in the event thecorresponding amplitude peak is at least 50%.
 20. The system of claim 14wherein the number of swimming strokes is determined by correlating xand y values sensed while the user swims between the starting and endingmarkers with a reference stroke selected from the x and y values, the xand y values being used by the head sensor to quantify movement in thecorresponding x and y directions.
 21. The system of claim 20 wherein:the correlating of the x and y values with the reference stroke includesfiltering the x and y values with the reference stroke to identifyamplitude peaks, wherein each amplitude peak correlating with at least a35% percentage of the reference stroke is counted as one of the numberof swimming strokes; and at least one of the at least one processorcharacterizes each of the number of swimming strokes as one of matchedand irregular, the matched strokes having amplitude peaks of greaterthan 79% and each irregular stroke having amplitude peaks of between 79%and 35%.
 22. The system of claim 1 wherein the number of leg kicks isdetermined by filtering the y values in the frequency domain to identifyamplitude peaks, wherein each amplitude peak is counted as one of thenumber of let kicks.
 23. The system of claim 22 wherein the y values arefiltered with a bandpass filter to identify the amplitude peaks in theevent the y values are characterized as sinusoidal and wherein the yvalues are filtered with a highpass filter to identify the amplitudepeaks in the event the y values are characterized as impulsed.
 24. Thesystem of claim 1 wherein at least one performance metric determined byat least one of the at least one processors identifies a length of timetaken for the user to swim one length of a pool based on movement sensedby the at least one head sensor.
 25. The system of claim 24 wherein thelength of time is counted from a start marker to an end marker, whereinthe start and end markers are determined based on movement sensed by theat least one head sensor, the start marker corresponding with movementindicating a first push-off event following a first head event, and theend marker corresponding with movement indicating a second push-offevent following a second head event.
 26. The system of claim 25 whereinthe first and second head events are determined in the event at leastone of the following conditions occur: (i) a head dip defined asz_(H)−x_(H)>T, wherein T is a threshold and x_(H) and z_(H) are valuesused by the head sensor to quantify movement in the corresponding x andz directions; (ii) a head throw defined as y_(H)>T_(d), wherein T_(d) isa dynamic threshold and y_(H) is a value used by the head sensor toquantify movement in the y direction; and (iii) a head roll defined asz_(H)<T_(Z) or S number of seconds before the head dip and z_(H)>T_(Z)for the S number of seconds following the same head dip, wherein T_(Z)is a threshold and z_(H) is a value used by the head sensor to quantifymovement in the z direction.
 27. The system of claim 25 wherein thefirst and second push-off events are determined in the eventz_(H)−x_(H)>T, wherein T is a threshold and x_(H) and z_(H) are valuesused by the head sensor to quantify movement in the corresponding x andz directions.
 28. A method of identifying a swimming stroke performed bya swimmer to be one of a crawl, back, breast, and butterfly stroke, themethod comprising: receiving a signal from each of one or more sensorsattached to at least one of a head, one or more legs and one or morearms of the swimmer, each signal representing movement of thecorresponding sensor relative to an x, y, and z axis; processing thesignals to identify the swimming stroke to be the crawl stroke in theevent at least one of: (i) the signals indicate the head to beorientated head-up and the swimmer's arms to be alternating; and (ii)the signals indicated the head to be orientated head-up and rollingside-to-side; processing the signals to identify the swimming stroke tobe the back stroke in the event the signals indicate the head to beorientated head-down; processing the signals to identify the swimmingstroke to be the breast stroke in the event at least one of: (i) thesignals indicate the swimmer's arms to be alternating and the swimmer'shand angle moves to a vertical position when swimming; and (ii) thesignals indicate the head to be orientated head-up, the head is notrolling side-to-side, and a stroke pattern best-fits with a known breaststroke pattern; processing the signals to identify the swimming stroketo be the butterfly stroke in the event at least one of: (i) the signalsindicate the swimmer's arms to be alternating and the swimmer's handangle remains in a flat position when swimming; (ii) the signalsindicate the head to be orientated head-up, the head is not rollingside-to-side, and a stroke pattern best-fits with a known butterflystroke pattern; and processing the signals to identify a number of legkicks performed by the swimmer between a starting marker and an endingmarker based on y-axis values generated with the at least one leg sensorto quantify movement in a y direction.
 29. A method of counting a numberof strokes performed by a swimmer from a movement signal generated by atleast one of an arm sensor, a leg sensor and a head sensor during a swumlap, the method comprising: identifying a dominate stroke period of theswum lap from the movement signal; selecting a portion of the movementsignal coinciding with one of the dominate periods to be a firstreference stroke; filtering the movement signal based on the firstreference stroke to identify a number of potential strokes; determiningthe number of strokes based on the identified number of potentialstrokes; and determining a number of leg kicks performed by the swimmerbetween a starting marker and an ending marker based on y valuesgenerated with the leg sensor to quantify movement in a y direction. 30.The method of claim 29 further comprising: averaging portions of themovement signal corresponding with two or more of the potential strokesto generate a second reference stroke; filtering the movement signalbased on the second reference stroke to identify a number of finalstrokes; and determining the number of strokes to be the number of finalstrokes having amplitudes greater than a threshold.
 31. The method ofclaim 29 further comprising centering phase of the first referencestroke to a phase of a first stroke of the swum length prior tofiltering the movement signal to identify the number of potentialstrokes.
 32. The method of claim 31 further comprising the centeringincluding aligning the reference stroke relative a beginning of thefirst stroke, the beginning of the first stroke being determined with anactivity-detection filter that filters a region of the first stoke toidentify a transition point from no activity to activity, the transitionpoint being the beginning of the first stroke.
 33. A method of countinga number of kicks performed by a swimmer based on a movement signalgenerated by at least one leg sensor during a swum lap, the methodcomprising: filtering the movement signal with a first plurality ofbandpass filters to generate a corresponding first plurality of filteredmovement signals; measuring an amplitude variance associated with eachof the first plurality of filters; in the event the amplitude varianceis less than a threshold, filtering the movement signal with a secondfilter to produce a second movement signal and counting the number ofkicks to correspond with a number of peaks identified within the secondmovement signal, the second filter being different from the plurality offilters; and in the event the amplitude variance is greater than thethreshold, identifying one of the first plurality of bandpass filtersmatching a dominate frequency of the movement signal, the bandpassfilter matching the dominate frequency of the movement signal being theone of the first plurality of bandpass filters generating the greatestamplitude output, and counting the number of kicks to correspond with anumber of peaks identified within the corresponding one of the firstplurality of filtered movement signals.
 34. The method of claim 33further comprising selecting the first plurality of filters to belowpass and the second filter to be highpass.
 35. A method ofdetermining a timestamp to reflect a point in time when a swimmerexecutes a push-off based on a signal that three-dimensionallyrepresents movement of the swimmer while swimming towards a wall priorto executing the push-off and way from the wall after executing thepush-off, the method comprising: processing the signal to identify afirst stroke occurring after the swimmer executes the push-off event;identifying a portion of the signal associated with swimmer movementprior to the first stroke for occurrence of a push-off event; in theevent a single push-off event is identified, determining the timestampto coincide with the single push-off event; in the event multiplepush-off events are identified, (i) determining the timestamp tocorrespond with an earliest one of the multiple push-off eventsoccurring within a push-off search region measured from occurring of asingle head event, (ii) determining the timestamp to correspond with anearliest one of the multiple push-off events occurring within anearliest one of multiple push-off search regions measured fromoccurrence of a corresponding multiple of head events, and (iii)determining the timestamp to correspond with an earliest one of themultiple push-off events occurring outside of any push-off searchregions measured from occurrence of a corresponding head event; anddetermining a number of leg kicks performed by the swimmer between astarting marker and an ending marker based on y values generated with aleg sensor worn on the swimmer to quantify movement in a y direction.36. The method of claim 35 further comprising determining push-offevents by high-pass-filtering X and Z axis data of the signal,subtracting Z axis data from X axis data, and finding peaks abovethreshold, each peak above the threshold being one push-off event. 37.The method of claim 35 further comprising determining head events to beany one of a head throw, a head dip, and a head roll wherein: the headdip is found by low-pass-filtering X and Z axis portion of the signal,subtracting Z from X, and finding a peak above a threshold, each peakbeing one head dip; the head roll is found by identifying a minimum in alow-pass-filtered Z axis portion of the signal; and the head throw eventis generated by squaring a low-pass-filtered Y axis portion of thesignal to find peaks above a threshold, each peak being one head throw.38. A non-transitory computer-readable medium having a plurality ofnon-transitory instructions operable with a processor to facilitatecounting a number of kicks performed by a athlete based on a movementsignal generated by at least one leg sensor worn on the athlete, themethod comprising: filtering the movement signal with a first pluralityof bandpass filters to generate a corresponding first plurality offiltered movement signals; measuring an amplitude variance associatedwith each of the first plurality of filters; in the event the amplitudevariance is less than a threshold, filtering the movement signal with asecond filter to produce a second movement signal and counting thenumber of kicks to correspond with a number of peaks identified withinthe second movement signal, the second filter being different from theplurality of filters; and in the event the amplitude variance is greaterthan the threshold, identifying one of the first plurality of bandpassfilters matching a dominate frequency of the movement signal, thebandpass filter matching the dominate frequency of the movement signalbeing the one of the first plurality of bandpass filters generating thegreatest amplitude output, and counting the number of kicks tocorrespond with a number of peaks identified within the correspondingone of the first plurality of filtered movement signals.